Abstract
The morphology and structure of 3D dendritic backbones are the essential to understand the neuronal circuitry and behaviors in the neurodegenerative diseases. As a big challenge, the research of extraction of dendritic backbones using image processing and analysis technology has attracted many computational scientists. This paper proposes a reliable and robust approach for automatically extract dendritic backbones in 3D optical microscopy images. Our systematic scheme is a gradient vector field based skeletonization approach. We first use self-snake based nonlinear diffusion, adaptive segmentation to smooth noise and segment the neuron object. Then we propose a hierarchical skeleton points detection algorithm (HSPD) using the measurement criteria of low divergence and high iso-surface principle curvature. We further create a minimum spanning tree to represent and establish effective connections among skeleton points and prune small and spurious branches. To improve the robustness and reliability, the dendrite backbones are refined by B-Spline kernel based data fitting. Experimental results on different datasets demonstrate that our approach has high reliability, good robustness and requires less user interaction.
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Xiao, L., Yuan, X., Galbreath, Z., Roysam, B. (2010). Automatic and Reliable Extraction of Dendrite Backbone from Optical Microscopy Images. In: Li, K., Jia, L., Sun, X., Fei, M., Irwin, G.W. (eds) Life System Modeling and Intelligent Computing. ICSEE LSMS 2010 2010. Lecture Notes in Computer Science(), vol 6330. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-15615-1_13
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DOI: https://doi.org/10.1007/978-3-642-15615-1_13
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